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    "# 空洞卷积 膨胀卷积 dilated convolution\n",
    "\n",
    "首先是诞生背景，在图像分割领域，图像输入到CNN（典型的网络比如FCN[3]）中，FCN先像传统的CNN那样对图像做卷积再pooling，降低图像尺寸的同时增大感受野，但是由于图像分割预测是pixel-wise的输出，所以要将pooling后较小的图像尺寸upsampling到原始的图像尺寸进行预测（upsampling一般采用deconv反卷积操作，deconv可参见知乎答案如何理解深度学习中的deconvolution networks？），之前的pooling操作使得每个pixel预测都能看到较大感受野信息。因此图像分割FCN中有两个关键，一个是pooling减小图像尺寸增大感受野，另一个是upsampling扩大图像尺寸。在先减小再增大尺寸的过程中，肯定有一些信息损失掉了，那么能不能设计一种新的操作，**不通过pooling也能有较大的感受野**看到更多的信息呢？**答案就是dilated conv。**\n",
    "\n",
    "作者：谭旭\n",
    "链接：https://www.zhihu.com/question/54149221/answer/192025860\n",
    "来源：知乎\n",
    "著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。\n",
    "\n",
    "![](imgs/dilated-conv.jpg)\n",
    "\n",
    "### 一张动图了解\n",
    "![Dilated Convolution with a 3 x 3 kernel and dilation rate 2](https://github.com/vdumoulin/conv_arithmetic/raw/master/gif/dilation.gif)\n",
    "Dilated Convolution with a 3 x 3 kernel and dilation rate 2"
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